accuracy-metrics-and-confusion-metrics.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8249624,"sourceType":"datasetVersion","datasetId":4890913}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-05-06T18:00:25.432876Z","iopub.execute_input":"2024-05-06T18:00:25.433761Z","iopub.status.idle":"2024-05-06T18:00:25.448211Z","shell.execute_reply.started":"2024-05-06T18:00:25.433728Z","shell.execute_reply":"2024-05-06T18:00:25.446987Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"/kaggle/input/aids-virus-infection-prediction/AIDS_Classification_15000.csv\n/kaggle/input/aids-virus-infection-prediction/AIDS_Classification_50000.csv\n/kaggle/input/aids-virus-infection-prediction/AIDS_Classification.csv\n/kaggle/input/aids-virus-infection-prediction/AIDS_Classification_5000.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.linear_model import LogisticRegression","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:00:17.575940Z","iopub.execute_input":"2024-05-06T18:00:17.576379Z","iopub.status.idle":"2024-05-06T18:00:17.582059Z","shell.execute_reply.started":"2024-05-06T18:00:17.576348Z","shell.execute_reply":"2024-05-06T18:00:17.581079Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"df=pd.read_csv(\"/kaggle/input/aids-virus-infection-prediction/AIDS_Classification_5000.csv\")\ndf.columns","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:00:30.662468Z","iopub.execute_input":"2024-05-06T18:00:30.665100Z","iopub.status.idle":"2024-05-06T18:00:30.711074Z","shell.execute_reply.started":"2024-05-06T18:00:30.665056Z","shell.execute_reply":"2024-05-06T18:00:30.710002Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"Index(['time', 'trt', 'age', 'wtkg', 'hemo', 'homo', 'drugs', 'karnof',\n       'oprior', 'z30', 'preanti', 'race', 'gender', 'str2', 'strat',\n       'symptom', 'treat', 'offtrt', 'cd40', 'cd420', 'cd80', 'cd820',\n       'infected'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"x=df[[ 'time' , 'trt', 'age', 'wtkg', 'hemo', 'homo', 'drugs', 'karnof',\n       'oprior', 'z30', 'preanti', 'race', 'gender', 'str2', 'strat',\n       'symptom', 'treat', 'offtrt', 'cd40', 'cd420', 'cd80', 'cd820'\n       ]]\ny=df[['infected']]","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:00:36.276354Z","iopub.execute_input":"2024-05-06T18:00:36.277374Z","iopub.status.idle":"2024-05-06T18:00:36.297070Z","shell.execute_reply.started":"2024-05-06T18:00:36.277323Z","shell.execute_reply":"2024-05-06T18:00:36.295979Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:00:40.824238Z","iopub.execute_input":"2024-05-06T18:00:40.824667Z","iopub.status.idle":"2024-05-06T18:00:40.835265Z","shell.execute_reply.started":"2024-05-06T18:00:40.824633Z","shell.execute_reply":"2024-05-06T18:00:40.834026Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"print(x_train.shape)\nprint(y_train.shape)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:00:42.738346Z","iopub.execute_input":"2024-05-06T18:00:42.738811Z","iopub.status.idle":"2024-05-06T18:00:42.745037Z","shell.execute_reply.started":"2024-05-06T18:00:42.738758Z","shell.execute_reply":"2024-05-06T18:00:42.743851Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"(4000, 22)\n(4000, 1)\n","output_type":"stream"}]},{"cell_type":"code","source":"x.info()","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:00:44.508683Z","iopub.execute_input":"2024-05-06T18:00:44.509118Z","iopub.status.idle":"2024-05-06T18:00:44.532282Z","shell.execute_reply.started":"2024-05-06T18:00:44.509082Z","shell.execute_reply":"2024-05-06T18:00:44.530933Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 5000 entries, 0 to 4999\nData columns (total 22 columns):\n #   Column   Non-Null Count  Dtype  \n---  ------   --------------  -----  \n 0   time     5000 non-null   int64  \n 1   trt      5000 non-null   int64  \n 2   age      5000 non-null   int64  \n 3   wtkg     5000 non-null   float64\n 4   hemo     5000 non-null   int64  \n 5   homo     5000 non-null   int64  \n 6   drugs    5000 non-null   int64  \n 7   karnof   5000 non-null   int64  \n 8   oprior   5000 non-null   int64  \n 9   z30      5000 non-null   int64  \n 10  preanti  5000 non-null   int64  \n 11  race     5000 non-null   int64  \n 12  gender   5000 non-null   int64  \n 13  str2     5000 non-null   int64  \n 14  strat    5000 non-null   int64  \n 15  symptom  5000 non-null   int64  \n 16  treat    5000 non-null   int64  \n 17  offtrt   5000 non-null   int64  \n 18  cd40     5000 non-null   int64  \n 19  cd420    5000 non-null   int64  \n 20  cd80     5000 non-null   int64  \n 21  cd820    5000 non-null   int64  \ndtypes: float64(1), int64(21)\nmemory usage: 859.5 KB\n","output_type":"stream"}]},{"cell_type":"code","source":"y.info()","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:00:47.828452Z","iopub.execute_input":"2024-05-06T18:00:47.829091Z","iopub.status.idle":"2024-05-06T18:00:47.839551Z","shell.execute_reply.started":"2024-05-06T18:00:47.829046Z","shell.execute_reply":"2024-05-06T18:00:47.838659Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 5000 entries, 0 to 4999\nData columns (total 1 columns):\n #   Column    Non-Null Count  Dtype\n---  ------    --------------  -----\n 0   infected  5000 non-null   int64\ndtypes: int64(1)\nmemory usage: 39.2 KB\n","output_type":"stream"}]},{"cell_type":"code","source":"Classify=LogisticRegression()\nClassify.fit(x_train,y_train)\nfrom sklearn.metrics import accuracy_score,confusion_matrix","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:01:44.511712Z","iopub.execute_input":"2024-05-06T18:01:44.512172Z","iopub.status.idle":"2024-05-06T18:01:44.624363Z","shell.execute_reply.started":"2024-05-06T18:01:44.512137Z","shell.execute_reply":"2024-05-06T18:01:44.622883Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n  y = column_or_1d(y, warn=True)\n/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"coef_ are:\",Classify.coef_)\nprint(\"intercept_ are:\",Classify.intercept_)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:02:56.680497Z","iopub.execute_input":"2024-05-06T18:02:56.682586Z","iopub.status.idle":"2024-05-06T18:02:56.689246Z","shell.execute_reply.started":"2024-05-06T18:02:56.682529Z","shell.execute_reply":"2024-05-06T18:02:56.688076Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"coef_ are: [[-3.61514743e-04 -1.45675967e-01 -7.15522080e-03  5.50593306e-03\n   7.33221558e-03  3.89667919e-02 -2.76859686e-02  3.90586931e-03\n   2.57823028e-02  2.17805021e-01  2.31896400e-04 -6.81287578e-02\n  -1.59379316e-02  2.12038022e-01  3.99367751e-01  6.66197923e-03\n  -4.49355502e-02 -9.26026182e-02 -2.80009136e-03 -2.15152136e-03\n  -1.67348202e-05 -1.60949106e-04]]\nintercept_ are: [0.00796888]\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred=Classify.predict(x_test)\nscore=accuracy_score(y_test,y_pred)\nprint(\"Score that my model acheive:\",score*100)\nconfusion=confusion_matrix(y_test,y_pred)\nprint(\"Confusion metrics:\",confusion)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:05:22.055273Z","iopub.execute_input":"2024-05-06T18:05:22.055650Z","iopub.status.idle":"2024-05-06T18:05:22.083843Z","shell.execute_reply.started":"2024-05-06T18:05:22.055623Z","shell.execute_reply":"2024-05-06T18:05:22.082378Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":"Score that my model acheive: 67.80000000000001\nConfusion metrics: [[596  80]\n [242  82]]\n","output_type":"stream"}]},{"cell_type":"code","source":"y_test.shape[0]","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:06:07.418642Z","iopub.execute_input":"2024-05-06T18:06:07.419119Z","iopub.status.idle":"2024-05-06T18:06:07.426831Z","shell.execute_reply.started":"2024-05-06T18:06:07.419086Z","shell.execute_reply":"2024-05-06T18:06:07.425299Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"1000"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.tree import DecisionTreeClassifier","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:08:43.494359Z","iopub.execute_input":"2024-05-06T18:08:43.494864Z","iopub.status.idle":"2024-05-06T18:08:43.501517Z","shell.execute_reply.started":"2024-05-06T18:08:43.494827Z","shell.execute_reply":"2024-05-06T18:08:43.499799Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"Disicion=DecisionTreeClassifier()\nDisicion.fit(x_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:09:26.087704Z","iopub.execute_input":"2024-05-06T18:09:26.088196Z","iopub.status.idle":"2024-05-06T18:09:26.157205Z","shell.execute_reply.started":"2024-05-06T18:09:26.088161Z","shell.execute_reply":"2024-05-06T18:09:26.156012Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"DecisionTreeClassifier()","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"Pred=Disicion.predict(x_test)\nprint(\"Accuracy of Dicesion Tree:\",accuracy_score(y_test,Pred)*100)\nprint(\"Model Confusion metrics is:\",confusion_matrix(y_test,Pred))","metadata":{"execution":{"iopub.status.busy":"2024-05-06T18:12:29.646848Z","iopub.execute_input":"2024-05-06T18:12:29.647371Z","iopub.status.idle":"2024-05-06T18:12:29.668991Z","shell.execute_reply.started":"2024-05-06T18:12:29.647338Z","shell.execute_reply":"2024-05-06T18:12:29.667808Z"},"trusted":true},"execution_count":40,"outputs":[{"name":"stdout","text":"Accuracy of Dicesion Tree: 59.199999999999996\nModel Confusion metrics is: [[468 208]\n [200 124]]\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}